I coded one game (set) each from the following matches: Illinois at Minnesota (box score, ESPN 3 video); South Carolina at Florida (box score); Nebraska at Texas (box score); Oklahoma at Texas A&M (box score); and Penn State at Michigan (box score).
As teams attempted to run their offense in immediate response to the opponent's serve, it was usually pretty easy to classify whether they were in or out of system. Certainly, if someone other than the setter made the second contact, or if the team was aced or made an overpass, it was out of system. I also considered a sequence to be out of system (although not as egregiously) if the setter had to tip (with one hand) or bump the ball to the hitter. In addition, I recorded whether the receiving team successfully sided-out (not just an immediate side-out, i.e., serve, pass, set, kill, but all side-outs, regardless of how long the rally lasted).
I clearly expected teams to exhibit greater side-out success rates when their initial response to the opponent's serve was in, as compared to out of, system. To get an idea of the magnitude of the difference, however, we needed some empirical data, hence the following analyses. The following chart (which you can click on to enlarge) contains the key information. The data should be considered only an approximation, as I sometimes missed a play or two per game (sometimes it was my fault due to a momentary lapse of attention, but other times things were outside of my control, such as a TV replay interrupting the beginning of the next point). Because the numbers for any one team would be too small for statistical analysis, I added up the data for each column for all the teams, thus producing aggregate figures.
When teams mounted an in-system (i.e., pass, set, spike) response to the opponent's serve, they sided-out nearly 63% of the time (102/163). In stark contrast, when the serve-receipt got out of system, the team sided-out only around 10% of the time (4/39). For statistically trained readers out there, this difference in percentages is highly significant via a chi-square test (X2 = 34.5, df = 1, p < .001).
It did not surprise me that teams rarely win the point when they start off out of system. It surprised me a little bit, though, that teams did not side-out more frequently when they mounted an in-system response to serve. A spike cleanly set up and delivered is no guarantee of winning the point, however, as the ball can be blocked or dug. As anyone who saw this past weekend's Penn State-Michigan match knows, there was a sequence in Game 1 during which the Wolverines consistently mounted in-system attacks -- and consistently got stuffed by the Nittany Lions!
Another thing I found interesting, albeit which must be qualified by the small number of observations, is the variation in how often teams got out of system. National No.1-ranked Florida never went out of system in the game I coded, whereas the Gators' tough serving knocked South Carolina out of system a whopping 10 times. On average, teams got out of system 3.9 times within the span of one game (set).
This investigation, like many of my previous ones, aimed to provide an initial look at a phenomenon, in this case out-of-system play, and put some ideas out there for operational measures and statistical analyses. Further research could examine whether particular servers are adept at getting the opponent out of system, as well as probe in/out-of-system status not just in response to the opponent's serve, but also to spikes and free balls.
UPDATE: This topic has generated some interesting discussion at VolleyTalk. Here's a link to the thread.
3 comments:
Interesting stuff. I think with a larger sample size, you will find that the out-of-system sideout percentage is a bit higher than 10%. Or perhaps at the high D1 level, maybe it isn't.
I did a study on this topic as part of a project for a college class. Sadly, I don't still have the data, but I remember that, with over 1000 serve reception data points, an in-system (defined as a "2" or "3" point pass) was about 4.5x as valuable as a 1-point pass. If I recall correctly, it was something like 15% to 65% or so. This was based off a study of D3/2 women's volleyball teams.
Basically the contention of the paper was that the standard pass grading system is flawed because the 0-1-2-3 system assigns arbitrary valuation that doesn't quite match the actual value of a pass. A 2-point pass is not twice as good as a 1-point pass; it is 3-4x as valuable. Likewise, a 3-point pass is not 1.5x as valuable, it is somewhere between 1.1-1.3x as valuable, although a cursory (read: small sample size) evaluation I did just casually watching a few world league matches suggests that it might be 1.4x or even close to 1.5x as valuable at the international men's level.
This sets up a variety of flawed situations. Consider this situation with two passers:
Passer A: 10 passing errors and 20 3-point passes. (20*3)/30 = 2.0
Passer B: 30 2-point passes. (30*2)/30=2.0
Data suggests that Player A's team will earn about 14-15 sideouts on those 30 passes, while Player B's team will earn 18-20. So Player B is a significantly better passer (4 or 5 points is an enormous difference over a match), yet they were graded the same.
On the flip side, imagine another situation:
Player A: 15 passing errors and 15 2-point passes. (15*2)/30 = 1.0
Player B: 30 1-point passes. (30*1)/30 = 1.0
Data suggests that Player A's team will earn about 9-10 sideouts while Player B's team will earn 3-5. So now, Player A is much more valuable, despite receiving the same valuation AND making drastically more passing errors.
These are extreme examples, but they illuminate a fundamental problem in how serve reception is evaluated: at any level higher then high school JV, there is really not much difference between a passing error and an out-of-system pass, nor is there all that much difference between a "good", reasonably in-system, pass, and a "perfect" pass.
A final example using more realistic numbers for a high-level college player team:
Player A: 20 2-point passes, 10 3-point passes. (20*2 + 10*3)/30 = 2.33
Player B: 20 3-point passes, 5 2-point passes, 4 1-point passes, 1 reception error. (20*3 + 5*2 + 4*1)/30 = 2.47
Player A's expected sideouts: 19
Player B's expected sideouts: 17.5
Is that a HUGE deal? No. But almost any coach around would tell you that Player B passed a better game (2.47 to 2.33, 67% perfect pass to 33% and only 1 reception error), yet it's quite likely that Player A helped his/her team more.
It's kind of like baseball, where for a long time, it was all batting average and home runs. Then people finally realized that just getting on base was the most important thing. Setters are good enough and hitters are athletic enough to run tempo on 2-point pass, so anything reasonably in-system such that the setter has at least 2 options so that the block can't leave early is all that really matters.
For clarification, I am defining "in-system" as a 2 or 3-point pass, and "out-of-system" as a 1-point pass.
I know this thread is 5 years old but I love the analysis! This shows how important the initial pass is. If the pass is weak, the odds are against the attack. I enjoyed Joseph's contributions, too.
This begs the question: if you're succeeding 65% of the time when in system, does that mean you're on offense or defense?
Thanks!
Post a Comment